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Accurate and Numerically Efficient r<sup>2</sup>SCAN Meta-Generalized Gradient Approximation

The Journal of Physical Chemistry Letters · 2020 · Vol. 11(19) · pp. 8208–8215
James W. FurnessAaron D. KaplanJinliang NingJohn P. PerdewJianwei Sun

Abstract

The recently proposed rSCAN functional [ <i>J. Chem. Phys.</i> 2019 150, 161101] is a regularized form of the SCAN functional [ <i>Phys. Rev. Lett.</i> 2015 115, 036402] that improves SCAN's numerical performance at the expense of breaking constraints known from the exact exchange-correlation functional. We construct a new meta-generalized gradient approximation by restoring exact constraint adherence to rSCAN. The resulting functional maintains rSCAN's numerical performance while restoring the transferable accuracy of SCAN.

Advanced NMR Techniques and ApplicationsMachine Learning in Materials ScienceAdvanced Chemical Physics StudiesConstraint (computer-aided design)MathematicsApplied mathematicsConstruct (python library)PhysicsMathematical analysisComputer scienceGeometry

Funding

  • Basic Energy Sciences
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